High- and Low-Frequency Correlations in European Government Bond Spreads and Their Macroeconomic Drivers

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. This paper combines two closely related papers, one by Simona Bo¤eli and Giovanni Urga previously circulated under the same title and one by Vasiliki Skintzi previously circulated under the title "Dynamic Component Correlation Models and Macroeconomic Determinants". We are very grateful to the Editor, Eric Ghysels, for having sponsored and encoraged us to merge the two contributions to produce a comprehensive paper which fully explores a wide range of MIDAS models. We wish to thank participants in the 24th (EC) Permanent repository link2 Conference, The Econometrics Analysis of Mixed Frequency Data (13-14 December 2013, University of Cyprus, Nicosia, Cyprus) in particular Eric Ghysels and Rossen Valkanov; in the 6th MAF Conference (22-24 April 2014, Lloyd's Baia Hotel, Vietri sul Mare, Italy); and in the 7th Annual Society for Financial Econometrics-SoFIE-Conference (11-13 June 2014, Rotman School of Management and the Global Risk Institute in Financial Services, Toronto, Canada), in particular Andras Fulop, Eric Ghysels and Robin Lumsdaine, for very useful comments and suggestions. Special thanks to …ve referees, an Associate Editor, and to Jan Novotny for having provided us with very insightful and useful comments which greatly helped to improve the paper. The usual disclaimer applies. Special thanks to Morningstar, in particular to Richard Barden, for having made available the rich and unique data set used in this paper. Simona Boffelli acknowledges …nancial support from the Centre for Econometric Analysis, Cass Business School, City University London, UK. AbstractWe propose to adopt high-frequency DCC-MIDAS models to estimate high-and lowfrequency correlations in the 10-year government bond spreads for Belgium, France, Italy, the Netherlands and Spain relative to Germany, from 1-June-2007 to the 31-May-2012. The high-frequency component, re ‡ecting …nancial market conditions, is evaluated at 15-minute frequency, while the low-frequency component, …xed through a month, depends on country speci…c macroeconomic conditions. We …nd strong links between spreads volatility and worsening macroeconomic fundamentals; in presence of similar macroeconomic fundamentals relative spreads move together; the increasing correlation in spreads during the burst of the sovereign debt crisis cannot be entirely ascribed to macroeconomic factors but rather to changes in market liquidity.

Tourism and economic growth: Multi-country evidence from mixed-frequency Granger causality tests

“…However, international TA is a floating variable and by aggregating a predictor variable into a lower frequency may conceal important predictive information. Therefore, we use the mixed-frequency VAR (MF-VAR) approach of Ghysels et al (2016) to congenialise different frequency variables within the same empirical model so as to overcome the temporal aggregation bias (see Bevilacqua et al, 2019; Boffelli et al, 2016; Ferrara and Guérin, 2018; Ghysels, 2016). To address the time-variability patterns, we extend the full-sample approach of Ghysels et al (2016) to a time-varying MF-VAR framework by using a rolling window method.…”

Determinants of credit default swaps implied ratings during the crisis: was sovereign risk mispriced?

“…Yuan and Pongsiri (2015) test the relevance of expectations concerning GDP growth, but fail to reach a conclusion. Differently,Boffelli et al (2017) conclude that expectations, measured by the difference between each country's Eurostat index of business confidence and the corresponding value for Germany, are relevant in explaining sovereign risk. However, this is likely to be capturing the effect of correlated omitted variables, given the authors' extremely parsimonious representation of macroeconomic fundamentals.…”

“…Our panel is strongly balanced. It comprises the periods between the final quarter of 2007 and the 1 st quarter of 2015 (T = 30 quarters), and 26 OECD countries (N = 26) 9 . Hence, the analysis was conducted with 780 observations per variable.…”

“…Given that these became unavailable, with the necessary level of detail, from 2013 onwards, we have used, for the subsequent periods, CDPs computed similarly by Markit® 10 . To ensure consistency with CMA 9 10 of the original EA countries (except for Finland), Australia, Czech Republic, Denmark, Estonia, Hungary, Iceland, Israel, South Korea, New Zealand, Norway, Poland, Slovak Republik, Slovenia, Sweden, UK and US. 10 For detailed information on Markit ® data see, in this journal, Oliveira and Santos (2015).…”

Regulatory changes and long-run relationships of the EMU sovereign debt markets: Implications for future policy framework

“…In particular, cross-country joint relationships of conditional variance and return correlations are found to be typically positive. Boffelli et al (2016) focused on both the high and low frequency correlations in European government bonds via DCC-MIDAS while considering their economic drivers. They find strong links between spreads' volatility and worsening macroeconomic fundamentals.…”